Python 现场心电图实时散点图显示混乱，如何清理？

我有一些数据字典，我已经成功地让python实时更新并绘制了它们。得到线是可以做到的，但现在我需要绘制一个散点图。我不知道如何设置实时更新数据，以使我可以绘制x2和y5，基本上，我习惯于分离每个数据集，因此目前我有散射2（x2）和散射5（y5）分别更新，但如何将它们绘制在一起，例如ax.散射（x2，y5），而不是ax.散射（x2）和另一条ax.散射线（y5）？同时绘制它们，但单独绘制会得到一个填充过多的散点图（可能它也保存了太多的值），并且它似乎没有以与成功绘制的信号相同的速度移动

import heartpy as hp
import numpy as np
import matplotlib.pyplot as plt

import matplotlib.animation as animation 
from collections import deque
from matplotlib.ticker import FuncFormatter
#from . import config 





sample_rate = 40
def init(): # initialization function: plot the background of each frame
   line.set_ydata([np.nan]*len(x))
   line1.set_ydata([np.nan]*len(x1))
   scatter2.set_xdata([np.nan]*len(x2))
   # scatter3.set_ydata([np.nan]*len(x3))
   # scatter4.set_ydata([np.nan]*len(x4))
   scatter5.set_ydata([np.nan]*len(y5))

   return line, line1, scatter2, scatter5, #scatter4, scatter5

def animate(i):
    read_fname = 'temp.1D'
    datax = np.loadtxt('temp.1D')
    wd, m = hp.process(datax, 40 ) #run analysis        
    hr = wd['hr'] 
    rm = wd['rolling_mean'] 
    yb = wd['ybeat']
    pl = wd['peaklist']
    rp = wd['removed_beats']
    rp_y = wd['removed_beats_y']
    #pl=ax.collections[pl].get_array()

    yhr = np.array(hr[:])
    yrm = np.array(rm[:])
    yyb = np.array(yb[:])
    ypl = np.array(pl[:])
    yrp = np.array(rp[:])
    yrp_y = np.array(rp_y[:])
    yhr = np.concatenate([yhr])
    yrm = np.concatenate([yrm])
    yyb = np.concatenate([yyb])
    ypl = np.concatenate([ypl])
    yrp = np.concatenate([rp])
    yrp_y = np.concatenate([rp_y])

    data.extend(yhr)
    data1.extend(yrm) 
    data2.extend(ypl)
  #  data3.extend(yrp)
   # data4.extend(yrp_y)
    data5.extend(yyb)
    

    line.set_ydata(data)
    line1.set_ydata(data1)
    scatter2.set_xdata(data2)
   # scatter3.set_ydata(data3)
    # scatter4.set_ydata(data4)
    scatter5.set_ydata(data5)


    print(yhr)
    print(yrm)
    print(ypl)

    print()
    return line, line1, scatter2, scatter5, #scatter4, scatter5

max_x = 250
max_y = 1700
#max_x2 = 1000
#max_y5 = 1000
data = deque(np.zeros(max_x),maxlen=max_x)
data1 = deque(np.zeros(max_x),maxlen=max_x)
data2= deque(np.zeros(max_x),maxlen=max_x)
#data3 = deque(np.zeros(max_x),maxlen=max_x)
#data4 = deque(np.zeros(max_x),maxlen=max_x) #hold the last 10 values
data5 = deque(np.zeros(max_y),maxlen=max_y)

x = np.arange(0,max_x)
x1 = np.arange(0,max_x)   
x2 = np.arange(0,max_x)
#x3 = np.arange(0,max_x)
#x4 = np.arange(0,max_x)
y5 = np.arange(0,max_y)

fig, ax = plt.subplots()
#fig, ax1 = plt.subplots()
ax.set_ylim(-500, max_y)
ax.set_xlim(0, max_x-1)
line, = ax.plot(x,  np.linspace(0,1000,250,endpoint = False))
line1, = ax.plot(x1, np.linspace(0,1000,250,endpoint = False))
#ax = plt.gca()
scatter2, = ax.plot(x2, marker="o", ls="", color = "green")
#scatter3, = ax.scatter(x3, np.linspace(0,50,1000))
#scatter4 = ax.scatter(x4, np.linspace(0,50,1000))
scatter5, = ax.plot(y5, marker="o", ls="")

#ax.grid()
#ax.relim()
#ax.autoscale_view(True,True,True)

ax.xaxis.set_major_formatter(FuncFormatter(lambda x, pos: '{:.0}s'.format(max_x -x -990)))

plt.xlabel('Seconds ago')



ani = animation.FuncAnimation(fig,animate,init_func=init,interval=25,blit=True,save_count = 10)




plt.show()